Novel camera calibration method based on invariance of collinear points and pole-polar constraint

To address the eccentric error of circular marks in camera calibration,a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper.Firstly,the centers of the ellipses are extracted,and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points.Subsequently,since the infinite lines passing through the centers of the marks are parallel,the other center projection coordinates are expressed as the solution problem of linear equations.The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed,and the results are utilized as the true image points to achieve the high precision camera calibration.As demonstrated by the simulations and practical experiments,the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks.The relevant results confirm the precision and robustness of the proposed approach.

A Review of RGB-D Camera Calibration Methods

RGB-D camera is a new type of sensor,which can obtain the depth and texture information in an unknown 3D scene simultaneously,and they have been applied in various fields widely.In fact,when implementing such kinds of applications using RGB-D camera,it is necessary to calibrate it first.To the best of our knowledge,at present,there is no existing a systemic summary related to RGB-D camera calibration methods.Therefore,a systemic review of RGB-D camera calibration is concluded as follows.Firstly,the mechanism of obtained measurement and the related principle of RGB-D camera calibration methods are presented.Subsequently,as some specific applications need to fuse depth and color information,the calibration methods of relative pose between depth camera and RGB camera are introduced in Section 2.Then the depth correction models within RGB-D cameras are summarized and compared respectively in Section 3.Thirdly,considering that the angle of the view field of RGB-D camera is smaller and limited to some specific applications,we discuss the calibration models of relative pose among multiple RGB-D cameras in Section 4.At last,the direction and trend of RGB-D camera calibration are prospected and concluded.

Camera calibration method for an infrared horizon sensor with a large field of view

Inadequate geometric accuracy of cameras is the main constraint to improving the precision of infrared horizon sensors with a large field of view(FOV).An enormous FOV with a blind area in the center greatly limits the accuracy and feasibility of traditional geometric calibration methods.A novel camera calibration method for infrared horizon sensors is presented and validated in this paper.Three infrared targets are used as control points.The camera is mounted on a rotary table.As the table rotates,these control points will be evenly distributed in the entire FOV.Compared with traditional methods that combine a collimator and a rotary table which cannot effectively cover a large FOV and require harsh experimental equipment,this method is easier to implement at a low cost.A corresponding three-step parameter estimation algorithm is proposed to avoid precisely measuring the positions of the camera and the control points.Experiments are implemented with 10 infrared horizon sensors to verify the effectiveness of the calibration method.The results show that the proposed method is highly stable,and that the calibration accuracy is at least 30%higher than those of existing methods.

Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced,and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.

Optical focal plane based on MEMS light lead-in for geometric camera calibration

The focal plane of a collimator used for the geometric calibration of an optical camera is a key element in the calibration process.The traditional focal plane of the collimator has only a single aperture light lead-in,resulting in a relatively unreliable calibration accuracy.Here we demonstrate a multi-aperture micro-electro-mechanical system(MEMS)light lead-in device that is located at the optical focal plane of the collimator used to calibrate the geometric distortion in cameras.Without additional volume or power consumption,the random errors of this calibration system are decreased by the multi-image matrix.With this new construction and a method for implementing the system,the reliability of high-accuracy calibration of optical cameras is guaranteed.

Camera Calibration Method Based on Self-made 3D Target

A camera calibration algorithm based on self-made target is proposedin this paper, which can solve the difficulty of making high precision 3Dtarget. Theself-made target consists of two intersecting chess board. With the classic scalemethod, the 3D coordinates of selected points in the target were derived from thedistance matrix. The element in distance matrix is the distance between every twopoints, which can be obtained by measurement. The spatial location precision ofpoints in the target was ensured by measurement instead of manufacturing, whichreduced the production cost and the requirements for the production accuracygreatly. Camera calibration was completed using 3D target based method. It canbe further extended to the applications where the target cannot be produced. Theexperimental results show the validity of this method.

Constructing a Virtual Large Reference Plate with High-precision for Calibrating Cameras with Large FOV

It is well known that the accuracy of camera calibration is constrained by the size of the reference plate,it is difficult to fabricate large reference plates with high precision.Therefore,it is non-trivial to calibrate a camera with large field of view(FOV).In this paper,a method is proposed to construct a virtual large reference plate with high precision.Firstly,a high precision datum plane is constructed with a laser interferometer and one-dimensional air guideway,and then the reference plate is positioned at different locations and orientations in the FOV of the camera.The feature points of reference plate are projected to the datum plane to obtain a virtual large reference plate with high-precision.The camera is moved to several positions to get different virtual reference plates,and the camera is calibrated with the virtual reference plates.The experimental results show that the mean re-projection error of the camera calibrated with the proposed method is 0.062 pixels.The length of a scale bar with standard length of 959.778mm was measured with a vision system composed of two calibrated cameras,and the length measurement error is 0.389mm.

Rigorous and integrated self-calibration model for a large-field-of-view camera using a star image

This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.

Correction Optimization of Lens Radial Distortion with Bending Measurement Function

In this paper, a distortion correction method with reduced complexity is proposed. With the singleparameter division model, the initial approximation of distortion parameters and the distortion center can be calibrated. Based on the distance from the image center to the fitting lines of the extracted curves, a bending measurement function with a weighted factor is proposed to optimize the initial value. Simulation and experiments verify the proposed method.

A New Photogrammetric Inspection Method for National Flags with Pentagrams

National flags are very important symbols of countries.They represent the countries’s authority and dignity of a country.However,there are some occasions where the flags were produced incorrectly(or as frauds)but still hung officially without a formal inspection.In this paper,we propose a photogrammetric inspection method for hanging national flags of China,for which only one single image is required to perform the inspection.The proposed method allows automatic estimation of the relative positions and orientation of the pentagrams,so exposure of inappropriate flags can be identified avoided.The method invokes a novel 2D geometric model of a pentagram(five-pointed star)to constrain an adjustment to estimate the camera’s exterior orientation parameters based on a single image of a statically hung flag.Conventional error parameters such as the radial distortion parameters are integrated into the pentagram model to form a calibration process to reduce the 3D reconstruction errors.Once the camera and the distortion parameters are estimated,the relative positions,orientations,and dimensions of all the five pentagrams can be readily computed with independent pentagram fitting so the flag quality can be verified using the national standard.More than 20 different hanging flags were captured to verify the proposed method.The results indicate that the method is flexible and accurate,with an accuracy of 1.1mm for the position/dimension,and 0.2°for the orientation on average.Since the method is based on the proposed geometric model of the pentagram,it can be readily adapted to form another system to verify other countries’national flags containing more than one pentagram.

3D Face Reconstruction Using Images from Cameras with Varying Parameters

In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system: we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence.

A method of 3D reconstruction based on panoramic cameras

In this paper,a method of 3D reconstruction from two images acquired by two panoramic cameras is presented.Firstly,the features of the reconstruction object detected in each image are matched through the DP matching method.Secondly,optical correction is carried out on two cameras,and the internal parameters of panoramic cameras can be calculated.Finally,according to the calibration method,the geometric relationship between corresponding points in space and in two panoramic images is deduced.The results indicate that the method of 3D reconstruction based on two panoramic cameras is simple,and the accuracy can reach 98.82%.

The role of machine intelligence in photogrammetric 3D modeling-an overview and perspectives

The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products.The photogrammetric industry offers engineering-grade hardware and software components for various applications.While some components of the data processing pipeline work already automatically,there is still substantial manual involvement required in order to obtain reliable and high-quality results.The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs.It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry,as well as its neighboring field computer vision.This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and‘intelligent’component to photogrammetry,computer vision and(to a lesser degree)to remote sensing.We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline:(1)data acquisition(2)georeferencing/interest point matching(3)Digital Surface Model generation(4)semantic interpretations,followed by conclusions and our insights.

Automatic Georeferencing of Images Acquired by UAV’s

This paper implements and evaluates experimentally a procedure for automatically georeferencing images acquired by unmanned aerial vehicles(UAV s) in the sense that ground control points(GCP) are not necessary. Since the camera model is necessary for georeferencing, this paper also proposes a completely automatic procedure for collecting corner pixels in the model plane image to solve the camera calibration problem, i.e., to estimate the camera and the lens distortion parameters. The performance of the complete georeferencing system is evaluated with real flight data obtained by a typical UAV.